Overview of Mining Spatial Data
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Mining Spatial Data
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Mining spatial databases and data warehouses
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Spatial DBMS
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Spatial Data Warehousing
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Spatial Data Mining
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Spatiotemporal Data Mining
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Generalizing Spatial
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Spatial data:
 Generalize detailed geographic points into
clustered regions, such as business,
residential, industrial, or agricultural areas,
according to land usage
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Require the merge of a set of geographic
areas by spatial operations
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What Is a Spatial Database System?
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Geometric, geographic or spatial data: space-related data
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Example: Geographic space (2-D abstraction of earth surface),
VLSI design, model of human brain, 3-D space representing the
arrangement of chains of protein molecule.
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Spatial database system vs. image database systems.
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Image database system: handling digital raster image (e.g.,
satellite sensing, computer topography), may also contain
techniques for object analysis and extraction from images and
some spatial database functionality.
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Spatial (geometric, geographic) database system: handling
objects in space that have identity and well-defined extents,
locations, and relationships.
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GIS (Geographic Information System)
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GIS (Geographic Information System)
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Analysis and visualization of geographic data
Common analysis functions of GIS
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Search (thematic search, search by region)
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Location analysis (buffer, corridor, overlay)
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Terrain analysis (slope/aspect, drainage network)
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Flow analysis (connectivity, shortest path)
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Distribution (nearest neighbor, proximity, change detection)
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Spatial analysis/statistics (pattern, centrality, similarity, topology)
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Measurements (distance, perimeter, shape, adjacency, direction)
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Spatial DBMS (SDBMS)
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SDBMS is a software system that
supports spatial data models, spatial ADTs,
and a query language supporting them
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supports spatial indexing, spatial operations
efficiently, and query optimization
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can work with an underlying DBMS
Examples
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Oracle Spatial Data Catridge
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ESRI Spatial Data Engine
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Modeling Spatial Objects
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What needs to be represented?
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Two important alternative views
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Single objects: distinct entities arranged in space each
of which has its own geometric description
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modeling cities, forests, rivers
Spatially related collection of objects: describe space
itself (about every point in space)
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modeling land use, partition of a country into
districts
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Modeling Single Objects: Point, Line and Region
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Point: location only but not extent
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Line (or a curve usually represented by a polyline, a
sequence of line segment):
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moving through space, or connections in space (roads,
rivers, cables, etc.)
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Region:
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Something having extent in 2D-space (country, lake,
park). It may have a hole or consist of several disjoint
pieces.
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Modeling Spatially Related Collection of Objects
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Modeling spatially related collection of objects: plane partitions and
networks.
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A partition: a set of region objects that are required to be disjoint
(e.g., a thematic map). There exist often pairs of objects with a
common boundary (adjacency relationship).
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A network: a graph embedded into the plane, consisting of a set of
point objects, forming its nodes, and a set of line objects describing
the geometry of the edges, e.g., highways. rivers, power supply
lines.
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Other interested spatially related collection of objects: nested
partitions, or a digital terrain (elevation) model.
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Spatial Data Types and Models
Field-based model: raster
data
 framework: partitioning
of space
 Object-based model: vector
model
 point, line, polygon,
Objects, Attributes
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Spatial Query Language
Spatial query language
 Spatial data types, e.g. point, line segment, polygon, …
 Spatial operations, e.g. overlap, distance, nearest
neighbor, …
 Callable from a query language (e.g. SQL3) of
underlying DBMS
SELECT S.name
FROM Senator S
WHERE S.district.Area() > 300
 Standards
 SQL3 (a.k.a. SQL 1999) is a standard for query
languages
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File Organization and Indices
SDBMS: Dataset is in the secondary storage, e.g. disk
 Space Filling Curves: An ordering on the locations in a
multi-dimensional space
 Linearize a multi-dimensional space
 Helps search efficiently
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Spatial Query Optimization
A spatial operation can be processed using
different strategies
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Computation cost of each strategy depends on
many parameters
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Query optimization is the process of
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ordering operations in a query and
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selecting efficient strategy for each operation
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based on the details of a given dataset
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Spatial Data Warehousing
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Spatial data warehouse: Integrated, subject-oriented, time-variant,
and nonvolatile spatial data repository
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Spatial data integration: a big issue
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Structure-specific formats (raster- vs. vector-based, OO vs.
relational models, different storage and indexing, etc.)
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Vendor-specific formats (ESRI, MapInfo, Integraph, IDRISI, etc.)
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Geo-specific formats (geographic vs. equal area projection, etc.)
Spatial data cube: multidimensional spatial database
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Both dimensions and measures may contain spatial components
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Dimensions and Measures in Spatial
Data Warehouse
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Dimensions
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non-spatial
 e.g. “25-30 degrees”
generalizes to“hot”
(both are strings)
spatial-to-nonspatial
 e.g. Seattle generalizes
to description “Pacific
Northwest” (as a string)
spatial-to-spatial
 e.g. Seattle generalizes
to Pacific Northwest (as
a spatial region)
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Measures

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numerical (e.g. monthly revenue
of a region)
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distributive (e.g. count, sum)
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algebraic (e.g. average)
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holistic (e.g. median, rank)
spatial

collection of spatial pointers
(e.g. pointers to all regions
with temperature of 25-30
degrees in July)
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Spatial-to-Spatial Generalization
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Generalize detailed
geographic points into
clustered regions, such as
businesses, residential,
industrial, or agricultural
areas, according to land
usage
Dissolve
Requires the merging of a
set of geographic areas by
spatial operations
Intersect
Merge
Clip
Union
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Example: British Columbia Weather
Pattern Analysis
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Input
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Output
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A map that reveals patterns: merged (similar) regions
Goals
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A map with about 3,000 weather probes scattered in B.C.
Daily data for temperature, precipitation, wind velocity, etc.
Data warehouse using star schema
Interactive analysis (drill-down, slice, dice, pivot, roll-up)
Fast response time
Minimizing storage space used
Challenge
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A merged region may contain hundreds of “primitive” regions
(polygons)
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Star Schema of the BC Weather Warehouse
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Spatial data warehouse
 Dimensions
 region_name
 time
 temperature
 precipitation
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Measurements
 region_map
 area
 count
Dimension table
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Fact table
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Spatial Association Analysis
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Spatial association rule: A  B [s%, c%]
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A and B are sets of spatial or non-spatial predicates
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Topological relations: intersects, overlaps, disjoint, etc.
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Spatial orientations: left_of, west_of, under, etc.
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Distance information: close_to, within_distance, etc.
s% is the support and c% is the confidence of the rule
Examples
1) is_a(x, large_town) ^ intersect(x, highway)  adjacent_to(x, water)
[7%, 85%]
2) What kinds of objects are typically located close to golf courses?
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Spatial Autocorrelation
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Spatial data tends to be highly self-correlated
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Example: Neighborhood, Temperature
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Items in a traditional data are independent of each
other, whereas properties of locations in a map are
often “auto-correlated”.
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First law of geography:
“Everything is related to everything, but nearby things are
more related than distant things.”
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Spatial Trend Analysis
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Function
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Detect changes and trends along a spatial dimension
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Study the trend of non-spatial or spatial data
changing with space
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Application examples
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Observe the trend of changes of the climate or
vegetation with increasing distance from an ocean
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Crime rate or unemployment rate change with regard
to city geo-distribution
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Spatial Cluster Analysis
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Mining clusters—k-means, k-medoids,
hierarchical, density-based, etc.
Analysis of distinct features of the
clusters
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Constraints-Based Clustering
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Constraints on individual objects
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Clustering parameters as constraints
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K-means, density-based: radius, min-# of points
Constraints specified on clusters using SQL
aggregates
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Simple selection of relevant objects before clustering
Sum of the profits in each cluster > $1 million
Constraints imposed by physical obstacles
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Clustering with obstructed distance
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Constrained Clustering: Planning ATM
Locations
C2
C3
C1
River
Mountain
Spatial data with obstacles
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C4
Clustering without taking
obstacles into consideration
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Mining Spatiotemporal Data
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Spatiotemporal data
 Data has spatial extensions and changes with
time
 Ex: Forest fire, moving objects, hurricane &
earthquakes
Automatic anomaly detection in massive moving
objects
 Moving objects are ubiquitous: GPS, radar, etc.
 Ex: Maritime vessel surveillance
 Problem: Automatic anomaly detection
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